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关键词： Design process   Case study   Maintenance cost   Reliability   Life cycle cost   Business models  

摘要： The aim of this paper is to make designers aware of the interactions that exist between decisions made during the design phase and their consequences on Life Cycle Costs (LCC) in the context of a predetermined business model. Based on the value analysis, LCC and computational method, the proposed methodology can serve as a practical guide to explain the different design strategies according to the business model requirements. Profitability requires the engineer to identify a balanced product architecture by varying parameters such as component reliability, operational availability, accessibility solutions and maintenance plans. A case study is presented, without calculation details, to compare two design solutions in terms of total cost. An industrial system consisting of three components in series is used. This method allows designers to adapt solutions to the economic model in which they will operate. This time-consuming and costly method is not recommended for all industrial products, but is relevant for technologically complex and long-lived products such as trains, wind turbines or manufacturing equipment.

关键词： cultural diversity   family secrets   family therapy   rituals   The Ackerman Institute  

摘要： The Ackerman Institute for the Family, established in 1960 by Dr Nathan Ackerman, stands as one of the oldest and most respected family therapy institutes in the United States. Ackerman pioneered the integration of systemic insights into group settings, emphasised the crucial role of family in therapy, and advocated for the advancement and acceptance of family therapy. 'The Ackerman' played a pivotal role in launching Family Process, the first journal dedicated to academic activities in family therapy. Diversity and inclusion have been central tenets of Ackerman philosophy, evident in its programs, training courses, and staff composition. This commitment has produced a veritable cadre of family therapy leaders who have contributed significantly to both the Ackerman Institute and the profession, influencing policy decisions and clinical practices. One of the most esteemed and respected thought leaders and innovators of our field, who played a vital role in the legacy of several institutions, including the Ackerman, is Dr. Evan Imber-Black. She served as a long-time faculty member and director of the Ackerman Center for Families and Health. Her expertise encompasses the exploration of family rituals and family secrets, with a focus on the importance of rituals in providing meaning, identity, and connection across diverse cultures and family life cycles. As editor of Family Process for 8 years, she highlights the importance of examining scholarly work in the context of cultures as a requirement - we do not stand outside the culture - we are active participants. This paper is based on a conversation with Dr Evan Imber-Black, held in June 2023, delving into her personal and professional experiences, her connection to the Ackerman Institute, and her influential work on family rituals and secrets. Through this exploration, the paper sheds light on her commitment to diversity and the lasting impact of Dr Imber-Black's contributions to family therapy.

关键词： ChatGPT   generative AI   cheating   quality assurance   university assessment  

摘要： Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a dual-anonymous "quality assurance" marking exercise across four end-ofmodule assessments across a distance university computer science (CS) curriculum. Each marker received five ChatGPT-generated scripts alongside 10 student scripts. A total of 90 scripts were marked;every ChatGPTgenerated script for the undergraduate modules received at least a passing grade (> 40%), with all of the introductory module CS1 scripts receiving a distinction (> 85%). None of the ChatGPT-taught postgraduate scripts received a passing grade (> 50%). We also present the results of interviewing the markers and of running our sample scripts through a GPT-2 detector and the TurnItIn AI detector, which both identified every ChatGPT-generated script but differed in the number of false positives. As such, we contribute a baseline understanding of how the public release of generative AI is likely to significantly impact quality assurance processes. Our analysis demonstrates that in most cases, across a range of question formats, topics, and study levels, ChatGPT is at least capable of producing adequate answers for undergraduate assessment.

关键词： Security   Autonomous vehicles   Encryption   Games   Internet of Things   Computer science   Sun   Internet of the autonomous vehicle   secure communication   tamper-resistant   identity-based broadcast signcryption  

摘要： As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.

关键词： Oscillators   Task analysis   Machine learning algorithms   Convergence   Computer science   Standards   Search problems   Bipartite networks   coarsening   label propagation   multilevel approach   multi-label propagation  

摘要： Complex machine learning tasks for knowledge discovery in networked data, such as community detection, node categorization, network visualization, and dimension reduction, have been successfully addressed by coarsening algorithms. It iteratively reduces the original network into a hierarchy of smaller networks, resulting in informative simplifications of the original network at various degrees of detail. Few of these algorithms, however, have been specially built to cope with bipartite networks. Besides of this, current coarsening algorithms present the following theoretical limitations that should be addressed: 1) A high-cost search strategy in dense networks;2) current coarsening algorithms are usually based on label propagation, which is limited to propagate single-labels;and 3) the synchronous label propagation scheme yields the cyclic oscillation problem. To overcome such limitations, we propose a coarsening algorithm based on multi-label propagation, which is more suitable for large-scale bipartite networks and allows a time-effective implementation. Furthermore, our proposal improves the standard semi-synchronous strategy and simultaneously propagates multiple labels to create the coarsened network representation. The empirical analysis of synthetic and real-world networks provides evidence that our coarsening strategy leads to significant gains regarding accuracy and runtime against standard techniques.

关键词： African palm weevil   rearing   Rhynchophorus phoenicis   substrate   temperature  

摘要： The African palm weevil Rhynchophorus phoenicis (Fabricius) is an alternative to conventional protein sources from livestock and poultry due to their rich nutritional content and ability to be reared on agricultural by-products. When establishing a production protocol for mass production of R. phoenicis larvae, it is imperative to understand the interaction between substrate and temperature on the development and reproductive success of the weevils. To determine the optimum temperatures for continuous production of R. phoenicis, the larvae were subjected to four temperatures (20 degrees C, 25 degrees C, 30 degrees C, and 35 degrees C) at a relative humidity of 70% on different substrates (elephant ear corm, peeled sugarcane, and palm yolk) in an incubator. The developmental duration of R. phoenicis larvae decreased with a temperature above 30 degrees C whilst percentage larval survival was high at 20 degrees C and 25 degrees C. The temperature with the fastest larval development and highest mortality was 35 degrees C. The optimal temperature from egg to the adult stage was observed at 25 degrees C. The oviposition and hatchability of eggs was high on elephant ear corm (EC), and palm yolk (PY) at 25 degrees C whilst the number of cocoons was high at 20 degrees C on decorticated coconut coir (DC). Thus, it is feasible to utilise the easily accessible substrates that interact well with temperature to produce optimum condition for the production of R. phoenicis larvae.

关键词： large language models   ChatGPT   artificial intelligence   controlled experiment   object-oriented programming   software engineering education  

摘要： The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students' performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.